A remote check image capture device is an example of an image source. As an example, the remote check image capture device may comprise a tabletop check scanner located at a teller station of a bank branch to allow a bank teller to scan an image of a check to be deposited by a bank customer. As another example, the remote check image capture device may comprise a mobile device which has a built-in digital camera for capturing an image of a check to be deposited. Yet as another example, the remote check image capture device may comprise an automated teller machine (ATM) at which an image of a check to be deposited can be captured. These are example types of remote check image capture devices and are, therefore, example types of recognition sources.
When a depositor desires to deposit a check from a remote location in a check deposit transaction, the depositor uses a remote check image capture device to capture image data which is representative of an image of the check. The captured check image data is electronically sent to a back office facility of a financial institution, such as a bank, for further processing to complete the remote check deposit transaction. More specifically, a recognition engine is used to perform image recognition on the image data received from the remote check image capture device. The recognition engine provides recognition results which include a confidence level as is known. The recognition results including the confidence level are associated with the image source (i.e., the remote check image capture device) which provided the image data which is representative of the check image.
In some applications, a single remote check image capture device may provide multiple image sources (i.e., multiple types of images) of the same check item. For example, an ATM may be capable of capturing images under any combination of different types of light such as black and white, grayscale, color, infrared, and ultraviolet light. Each image source provides its own associated recognition results including confidence level.
A confidence level associated with an image source is specific to only that particular image source. A confidence level specific to one image source cannot be compared to a confidence level specific to another image source. Accordingly, a “best read” of recognition results from two different image sources cannot be obtained by comparing a confidence level associated with one image source and a confidence level associated with the other image source. It would be desirable to provide a method of processing image data from the two different image sources to provide normalized confidence levels which can be compared with each other so that the image source providing the “best read” of recognition results can be identified and selected.